Research On Path Planning And Algorithm Of Automated Arc Additive Manufacturing

Robotic wire arc additive manufacturing(WAAM)refers to the addition of material layers rather than conventional material reduction manufacturing techniques,the part is constructed by depositing a layer of material through a 3D CAD model.But now robotic arc additive manufacturing software rely on secondary development of existing software.This research aims to develop a wire arc welding additive manufacturing software through java language,which realizes the WAAM from STL models input to directly generated components.The main steps in the process planning of robotic automated additive manufacturing include sll model slicing to generate 2.5D layers,generating deposition paths for these layers,and determining welding parameters(wire feeding speed,walking speed,dry elongation,etc.)associated with the deposition path.Post-processing the workpiece.The specific steps are as follows:a three-dimensional solid model is established by CAD software,and an appropriate angle and step size output STL model is selected,and is cut into a set of 2.5D layers along the additive direction by a slicing program.The existing slicing algorithms mainly include equal-thickness slicing,adaptive slicing and multi-directional slicing algorithms.After the slicing is completed,each layer of sedimentary paths is calculated by a path planning program.Since arc-welded wire additive manufacturing systems produce much larger deposits than powder-based additive manufacturing systems,the deposition path is significantly affected by the geometric complexity of the components and the materials selected.The developed central axis conversion strategy based on Voronoi diagram can produce a void-free deposition path and solve the problem of complex component deposition.The weld bead model is established by the artificial neural network model,and a database is provided for the welding control,and the associated welding parameters are selected according to the desired path height and width to ensure seamless deposition of the path.In this study,an artificial neural network single-channel model was established by arc welding of stainless steel materials,and an optimal welding parameter was determined by experiments for the general additive manufacturing process.Both the artificial neural network model and the multi-bead overlap model are integrated into the path planning method,allowing the optimal welding parameters to be automatically matched corresponding to the generated offset path.Finally,the deposition tool is machined by a series of post-processing operations to produce finished parts having the required dimensional tolerances.The processing path can be determined by offsetting the deposition path and can be post-processed while depositing a certain height,which avoids re-fixation of the component and simplifies the post-processing process.